The present invention relates to speech recognition, and in particular, to systems, methods, and devices for location-based, context-driven speech recognition.
Unless otherwise indicated herein, the approaches described in this section are not prior art to the claims in this application and are not admitted to be prior art by inclusion in this section.
The popularity of speech recognition as a means for interacting with computing devices continues to increase. This is especially true for mobile computing device. As the form factor of such mobile computing devices shrink, the amount of space available for the various components of the device also shrinks. The effect of the reduced space is typically a demand for such constituent parts to be further miniaturized. However, the size of various aspects of a small mobile computing device, such as the user interface, can be reduced only so much before it becomes difficult to use or completely ineffective. For example, physical buttons, like those on a physical QWERTY keyboard, or graphical user interfaces with various interactive and adaptive controls displayed on a touch screen, rapidly lose their functionality and effectiveness as they are made so small that users can not physically operate them. Similarly, the display size on such small form factor devices are limited in the amount and type of information they can reasonably display to user with otherwise normal eye sight.
Furthermore, many existing and emerging government regulations are directed toward limiting the use of various types of mobile computing devices while operating motor vehicles. For example, many jurisdictions have implemented mandatory hands-free operation of mobile telephones and have completely prohibited sending text messages while driving.
To address such regulatory and size limitations, many solutions have implemented various types of speech recognition and voice synthesis features. Most of such systems use large databases of recognizable vocabularies in order to address any and all possible scenarios in which a user may wish to interact with the computing device. Such large-scale recognizable vocabularies typically require considerable computing resources that are not available on small, mobile, battery-operated computing devices. To address this particular limitation, most contemporary mobile speech recognition systems simply send the voice command data to a central or cloud-based speech recognition computer that has the requisite computing resources to effectively handle large-scale recognizable vocabulary databases. The remote speech recognizer then sends the results back to the mobile computing device over the network. Such networked mobile computing speech recognition systems can only work when there is available and adequate wireless data bandwidth over which to send and receive the necessary speech recognition related data.
Other systems, in an effort to work around the requirement for available and adequate bandwidth for centralized processing of speech recognition commands, have implemented use of various task or device specific recognizable vocabularies to reduce the requisite processing power of a standalone mobile computing device. By reducing the expected recognizable vocabulary, remote computing device need only consider a limited number of possible recognizable commands. While effective in some scenarios, such limited recognizable vocabularies are typically static and do not allow the user or the remote computing device to adapt to new or changing scenarios or environmental conditions.
Thus, there is a need for improved speech recognition in remote and standalone mobile computing devices. The present invention solves these and other problems by providing systems, methods, and apparatuses for location-based context-driven speech recognition.